Transforming Data Pipelines to Power Faster Credit Decisions
Capital OnePhoton · Delta LakeCapital One's data and engineering teams built a centralized Feature Hub on Databricks, using Photon as the compute engine and Delta Lake as the foundation, to serve as a single environment for historical and operational feature pipelines supporting its credit line increase program and credit decisioning models. The hub curates records across millions of accounts to provide a unified customer view for both modeling and daily decisioning. Capital One reports 60x faster compute performance for large-scale backfills and an 80% decrease in time and cost per job after migrating production pipelines to Databricks, shortening feature development and deployment from prior timelines to weeks.
Overview
Capital One's data and engineering teams built a centralized Feature Hub on Databricks, using Photon as the compute engine and Delta Lake as the foundation, to serve as a single environment for historical and operational feature pipelines supporting its credit line increase program and credit decisioning models. The hub curates records across millions of accounts to provide a unified customer view for both modeling and daily decisioning. Capital One reports 60x faster compute performance for large-scale backfills and an 80% decrease in time and cost per job after migrating production pipelines to Databricks, shortening feature development and deployment from prior timelines to weeks.
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Inspect the highlighted sourceThe challenge
Capital One's credit line increase program relies on intelligent and trusted data and insights to enable financial empowerment for its customers, but growing data volumes and complex pipelines led Capital One to establish a new centralized feature hub to further streamline data management and enhance performance.
The solution
Capital One's data and engineering teams built a centralized Feature Hub on Databricks, using Photon as the compute engine and Delta Lake as the foundation, as the single environment for both historical and operational feature pipelines. It curates records across millions of accounts, providing a 360-degree view of customer insights that supports both modeling and daily decisioning. Engineers used Databricks APIs to automate job triggers, monitoring and reruns, while autoscaling clusters matched resources to workloads.
Reported business value
Capital One improved job completion speeds by 60X and saw an 80% decrease in time and cost per job after migrating production pipelines to Databricks, with developing and deploying new model features, including full historical backfills, now taking only weeks.
Sources
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